Closed zhixuanli closed 7 months ago
BTW, could you please tell me about the training time of your experiments on a single 3090 GPU and h36m dataset as mentioned in the paper?
On my side, this setting will cost about 30 hours in total.
Hi, happy Friday, and have a nice weekend.
Could you please take a look at this question when you have time? Thank you so much! :)
BTW, could you please tell me about the training time of your experiments on a single 3090 GPU and h36m dataset as mentioned in the paper?
On my side, this setting will cost about 30 hours in total.
On a single 3090 GPU, the time overheads are as follows: mesh ● 3dpw: 30min/epoch, about half a day ● h36m: 1h25min/epoch, about a day and a half pose ● 3dpw: 1h40min/epoch ● h36m: 25min/epoch
Hi authors,
It's me again. Happy New Year!
I'm just wondering if this copy of the codes supports using multi-GPU. I have set the GPU ids to
0,1
in the command file, which ispython ./main/train.py --cfg ./config/train_mesh_h36m.yml --gpu 0,1
. But still, only the id 0 GPU is used.Is there any way to use two or more GPUs to accelerate the training since at present it will take about 20 hours for the training of the second stage for the h36m dataset?
Thanks again for all your support.
You could try to add 'self.model = nn.DataParallel(self.model)' in Line1, and Line2, then run the command with multi-GPUs (I have tried it and it could work).
BTW, could you please tell me about the training time of your experiments on a single 3090 GPU and h36m dataset as mentioned in the paper? On my side, this setting will cost about 30 hours in total.
On a single 3090 GPU, the time overheads are as follows: mesh ● 3dpw: 30min/epoch, about half a day ● h36m: 1h25min/epoch, about a day and a half pose ● 3dpw: 1h40min/epoch ● h36m: 25min/epoch
The training time of yours and mine are almost the same. Thanks for sharing.
Hi authors, It's me again. Happy New Year! I'm just wondering if this copy of the codes supports using multi-GPU. I have set the GPU ids to
0,1
in the command file, which ispython ./main/train.py --cfg ./config/train_mesh_h36m.yml --gpu 0,1
. But still, only the id 0 GPU is used. Is there any way to use two or more GPUs to accelerate the training since at present it will take about 20 hours for the training of the second stage for the h36m dataset? Thanks again for all your support.You could try to add 'self.model = nn.DataParallel(self.model)' in Line1, and Line2, then run the command with multi-GPUs (I have tried it and it could work).
This is great. Thanks!
BTW, could you please tell me about the training time of your experiments on a single 3090 GPU and h36m dataset as mentioned in the paper? On my side, this setting will cost about 30 hours in total.
On a single 3090 GPU, the time overheads are as follows: mesh ● 3dpw: 30min/epoch, about half a day ● h36m: 1h25min/epoch, about a day and a half pose ● 3dpw: 1h40min/epoch ● h36m: 25min/epoch
hello, i would ask the time that "img_db = joblib.load(db_file)" in the dataset.py when load h36m,since i run the code and find it stay in this step. thank you!
Hi authors,
It's me again. Happy New Year!
I'm just wondering if this copy of the codes supports using multi-GPU. I have set the GPU ids to
0,1
in the command file, which ispython ./main/train.py --cfg ./config/train_mesh_h36m.yml --gpu 0,1
. But still, only the id 0 GPU is used.Is there any way to use two or more GPUs to accelerate the training since at present it will take about 20 hours for the training of the second stage for the h36m dataset?
Thanks again for all your support.